Progress in video anomaly detection research is currently slowed by small datasets that lack a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move this research effort forward by introducing a large and varied new dataset called Street Scene, as well as two new evaluation criteria that provide a better estimate of how an algorithm will perform in practice. In addition to the new dataset and evaluation criteria, we present two variations of a novel baseline video anomaly detection algorithm and show they are much more accurate on Street Scene than two state-of-the-art algorithms from the literature.
@article{arxiv.1902.05872,
title = {Street Scene: A new dataset and evaluation protocol for video anomaly detection},
author = {Bharathkumar Ramachandra and Michael Jones},
journal= {arXiv preprint arXiv:1902.05872},
year = {2020}
}